Method for affinity scoring of peptide/protein complexes

ABSTRACT

The present invention is related to a quantitative structure-based affinity scoring method for peptide/protein complexes. More specifically, the present invention comprises a method that operates on the basis of a highly specific force field function (e.g. CHARMM) that is applied to all-atom structural representations of peptide/receptor complexes. Peptide side-chain contributions to total affinity are scored after detailed rotameric sampling followed by controlled energy refinement. The method of the invention further comprises a de novo approach to estimate dehydration energies from the simulation of individual amino acids in a solvent box filled with explicit water molecules and applying the same force field function as used to evaluate peptide/receptor complex interactions.

This application is a Divisional Application of U.S. Ser. No.11/568,108, filed 18 Oct. 2007, which is a U.S. National StageApplication of PCT/BE2005/000052, filed 21 Apr. 2005, which claimsbenefit of Serial No. 04447103.5, filed 21 Apr. 2004 in the EPO andwhich applications are incorporated herein by reference. A claim ofpriority to all, to the extent appropriate is made.

FIELD OF THE INVENTION

The present invention is related to a quantitative structure-basedaffinity scoring method for ligand/protein complexes such aspeptide/protein complexes. More specifically, the present inventioncomprises a method that operates on the basis of a highly specific forcefield function (e.g. CHARMM) that is applied to all-atom structuralrepresentations of peptide/receptor complexes. Peptide side-chaincontributions to total affinity are scored after detailed rotamericsampling followed by controlled energy refinement. The method of theinvention further comprises a de novo approach to estimate dehydrationenergies from the simulation of individual amino acids in a solvent boxfilled with explicit water molecules and applying the same force fieldfunction as used to evaluate peptide/receptor complex interactions.

BACKGROUND OF THE INVENTION

Peptides are important regulatory molecules involved in a variety ofbiological mechanisms. Their function is generally determined byprocessing kinetics, interaction specificity and, more fundamentally,binding affinity. A thorough understanding of the contributions relevantfor stable complex formation may form the basis of experimentalrationalization, detection of novel ligands and optimization of leadcompounds. Predictive structure-based methods can be very helpful,provided that they are of sufficiently high accuracy.

Structure-based binding studies face two major technical barriers. Thefirst resides in the prediction of accurate 3D-structures forpeptide/receptor complexes. Peptides are conformationally very flexiblesince most of their chemical bonds are subject to free rotation. Apartial solution is to perform flexible docking using predefinedrotamers. Yet, deviation from ideal rotameric states and small-scaleflexibility due to bond angle bending present additional difficulties.Occasionally, peptides adopt variable binding modes, partly bulge outinto solvent or let some flanking residues hang out of the binding site.Yet, it is often observed that one or more peptide side-chains areanchored into well-shaped pockets in the interface surface. In suchcases, conformational flexibility is limited, which facilitatesstructure-based analysis.

The second problem is how to derive accurate binding affinities fromexperimental or modeled representations. Even short peptides easilycontain more than a hundred atoms, making thousands of small pairwiseatomic interactions. Further, ligand/receptor interfaces are rarelyoptimally packed and can include multiple water molecules. Finally,binding affinity depends on thermodynamic properties of the bound andfree states of the molecules involved.

In view of these complications, structure-based affinity scoring methodsinvariably include approximations and/or a parameterization step whereinphysically relevant effects are captured into tunable parameters. Adistinction should be made between statistical and empirical methods.Statistical, or knowledge-based scoring methods operate on the basis ofatom (or group) contact potentials derived from known proteinstructures. Empirical, or partitioning methods work with predefinedphysical energy terms, represented by parameterized mathematicalequations that are optimized against experimental data. In view of theinevitable training step, validation on independent data is required.Here, it is not uncommon that methods performing relatively well on datasimilar to the training set are significantly less accurate on moredivergent datasets or must even be retrained. Transferability thereforeremains an important and delicate matter. Transferability is definedherein as the use of one and the same scoring function for differentreceptor/ligand systems.

The present invention is related to the binding characteristics ofanchor residues within peptide ligands of receptor molecules, e.g. humanleukocyte antigen (HLA) complexes. HLA class I molecules areimmunologically important receptors involved in specific recognitionbetween cytotoxic T lymphocytes and pathogen-infected cells.Pathogen-derived peptides, known as antigens, are mostly 8-10 residueslong. Structural information from the Protein Data Bank (PDB) isavailable for an increasing number of receptor subtypes (at presentabout ten). Common features of these complexes are the stronginteractions between receptor side-chains and the N- and C-terminal endsof the peptide backbone. The side-chains of peptide residues P2 (or,occasionally, P3) and P9 (for 9-mers) are located into well-formedpockets named B (or D) and F, respectively. Hence, the side-chainorientation of anchor residues and their structural context arerelatively fixed. Yet, there is a significant variety in anchorproperties among different subtypes. Finally, anchor residues aredominant contributors to total affinity and greatly determine bindingspecificity. For all these reasons, anchor residues in HLA class Icomplexes are ideally suited to develop or test novel affinity scoringmethods.

The present invention relates to the identification of thephysico-chemically most relevant affinity determinants. Possiblecontributions like contact-based potentials, weight-adaptedconformational energy terms, shape complementarity, hydrophobiccorrections and different entropical components may yield good resultsbut poor transferability. Because of the danger of overparameterization,erroneous assignment of either false or redundant contributions islikely. Underparameterization, in particular of conformational strain,is another problem. The inventors therefore examined the possibility todevelop a scoring function based exclusively on an established forcefield function. The principal advantage of force field based approachesis that different physico-chemical interactions can be computed in aconsistent way.

SUMMARY OF THE INVENTION

The present invention concerns a method for determining an affinityscore of a binding protein/ligand complex such as a MHC receptor/ligandcomplex, said score advantageously being based, advantageously beingprimarily or solely based on structural data and a force field.

An affinity score can equally be determined for the anchor residues ofsaid binding protein/ligand complex such as a MHC receptor/ligandcomplex.

In another embodiment the present invention comprises the methodaccording to any of the previous embodiments whereby said affinity scorerepresents a combination of desolvation energy and protein-ligandcomplex energy, advantageously receptor-ligand complex energy, whereinboth said energies advantageously are derived from the same force field.

Advantageously, the affinity score and method of the present inventionis transferable to different protein/ligand systems, advantageously todifferent receptor/ligand systems, without reparameterisation.

The present invention in particular relates to a method for determiningan affinity score of a protein/ligand complex, characterised in thatsaid score is based on, advantageously primarily based on,advantageously solely based on structural data and a force field, thismethod comprising the steps of

(a) calculating a ligand-solvent interaction energy from a structuralrepresentation of the ligand placed in a box filled with explicitsolvent molecules;

(b) calculating a ligand-protein interaction energy from a structuralrepresentation of the ligand placed in the binding site of the protein;and

(c) calculating said affinity score by subtracting the ligand-solventinteraction energy of step (a) from the ligand-protein interactionenergy of step (b).

Preferably conformational strain energies are also taken into account.Accordingly, the present invention further relates to a method fordetermining an affinity score of a protein/ligand complex, characterisedin that said score is based on, advantageously primarily based on,advantageously solely based on structural data and a force field, thismethod comprising the steps of

(a) calculating a ligand-solvent interaction energy from a structuralrepresentation of the ligand placed in a box filled with explicitsolvent molecules;

(b) calculating a ligand-protein interaction energy from a structuralrepresentation of the ligand placed in the binding site of the protein;

(c) calculating a conformational strain energy for the protein/ligandrepresentation of step b; and

(d) calculating said affinity score by subtracting the ligand-solventinteraction energy of step (a) and the conformational strain energy ofstep (c) from the ligand-protein interaction energy of step (b).Advantageously the conformational strain energy of step (c) herein iscalculated as the difference between, on the one hand, the sum of theconformational energies of the ligand and protein in an unboundreference state and, on the other hand, the sum of the conformationalenergies of the ligand and the protein in the protein/ligandrepresentation of step (b). It is thus possible to calculateconformational strain energies sufficiently fast and in a way that theyare compatible with the force field used.

In a method according to the invention an affinity score advantageouslyis calculated (solely) from structural data and a force field. The forcefield may be any force field known in the art.

Advantageously exactly the same force field function is used to deriveligand-protein interaction energies; ligand-solvent interactionenergies, also called (de)solvation energies, (de)hydration energies;and possibly also conformational strain energies. (De)solvation energiesare advantageously derived from simulations of amino acid modelcompounds in an explicit solvent environment, for instance an explicitwater environment. Potential inconsistencies between solvent termsderived from experimental data and intra-complex terms based on theforce field are thus avoided. Advantageously the solvent in step (a) ofthe above methods exclusively consists of water molecules.

Advantageously, the protein/ligand representation of step (b) in amethod according to the invention is derived from an experimentallydetermined structure.

Alternatively the protein/ligand representation of step (b) may begenerated by computer modelling. Advantageously, the computer modellingcomprises an amino acid side-chain modelling step and/or an energyminimisation step.

Advantageously the representation of the ligand in the solvent box ofstep (a) of a method according to the invention is derived from anexperimentally determined structure.

Alternatively the representation of the ligand in the solvent box ofstep (a) may be generated by computer modelling.

Advantageously the computer modelling comprises an amino acid side-chainmodelling step and/or an energy minimisation step.

The methods of the invention are highly suitable for affinity scoring ofprotein-ligand complexes or receptor-ligand complexes. The protein towhich the ligand binds can be a receptor such as e.g. a MHC receptor, aHLA receptor, but it may also be an antibody. The antibody may be apolyclonal or a monoclonal antibody or a fragment thereof that iscapable of forming a 3-D structure (a macromolecule) to which a ligandcan bind.

Advantageously the ligand is a peptide, a small molecule or apharmacophore. The term “ligand” in the context of the present inventioncan refer to part of a ligand, such as the anchor residues of a ligand.The term “ligand” can refer in particular to any part of a peptide,including a side chain, backbone moiety, chemical group, . . . via whichthe ligand can interact with/bind to its binding protein or receptor.

A method of the invention is highly suited to calculate an affinityscore for the anchor residues of a ligand, but may also be used todetermine affinity scores for non-anchor residues so that a score can bedetermined for the whole of a ligand.

Advantageously, experimental data are used to verify predicted results.

The scoring functions of the invention are easily transferable todifferent protein/ligand complexes, as demonstrated below, without theneed of reparameterisation.

SHORT DESCRIPTION OF THE DRAWINGS AND TABLES

The FIG. 1 represents computed hydration energies for amino acidside-chains in a water box. Contributions for van der Waals (blackbars), H-bonding (gray) and electrostatic interactions (white) are givenseparately. Values are further subdivided per chemical group present ina side-chain and are therefore useful as group solvation parameters(GSP). Nine different functions are considered: (1) aliphaticC_(x)H_(y); (2) aromatic C_(x)H_(y); (3) aromatic N_(x)H_(y); (4)hydroxyl, OH; (5) sulphur/sulphydryl, S/SH; (6) charged amine, NH₃ ⁺;(7) carboxyl, COO⁻; (8) amide, CONH₂; and (9) guanidinium, NHC(NH₂)₂ ⁺atoms. Thus, each side-chain is composed of maximally three groups.Values for the aliphatic moieties (all residue types) are indicated bythin outlines. The second chemical moiety, if any, is indicated by thickoutlines. The third moiety (only Tyr, Trp and His) again by thinoutlines.

The FIG. 2A represents energy contributions calculated for differentside-chains placed at P2 in the pA1a/A2 complex. White bars, desolvationenergies; light gray bars, strain terms; dark gray bars, sum ofintra-complex interactions. Total energies are indicated by tiny blackbars; they correspond to the scores in column 3 of Table I. The FIG. 2Brepresents a detail of Gln interactions with the A2 receptor; 1, firstgroup (aliphatic moiety); 2, second group (amide function); V, van derWaals; H, H-bonds; E, electrostatic energy. Numeric values are computedinteraction energies in kcal/mol units. 1V:−6.1, 1H:0.0, 1E:0.2,2V:−9.6, 2H:−2.6, 2E: −2.9.

The FIG. 3 represents a comparison of anchor profiles for HLA-A1 betweenPepScope and Bimas predictions. Amino acid preferences for the anchorpositions indicated at the left are listed in decreasing order ofpreference. Strong and non-preferred residues are indicated in bold anditalics, respectively. The corresponding cutoffs are −2.5 and 0 kcal/molfor PepScope and 2 and 0.1 units for Bimas.

Table I gives Predicted vs Experimental Affinities of FLSKQYMTL Mutants.

Table II shows HLA-B7 Anchor Specificity.

The invention will be described in further details in the followingexamples by reference to the enclosed drawings and tables, which are notin any way intended to limit the scope of the invention as claimed. Themethods here described in more details for HLA receptors is applicableto other binding protein-ligand complexes.

DETAILED DESCRIPTION OF THE INVENTION

The present invention describes the development of a novel affinityscoring method here named “PepScope”. The latter forms part of theEpiBase™ platform for T-cell epitope identification (pending patentapplication WO03/105058 which is incorporated herein by reference). TheCHARMM force field was selected as the sole input function to PepScope.No parameterization steps were performed, except at the level of theprotocol (e.g. the number of energy minimization steps, cutoff distancesand model preparation strategy). Exactly the same force field functionwas used to derive (de)hydration energies from simulations of amino acidmodel compounds in explicit water environment. Potential inconsistenciesbetween solvent terms derived from experimental data and intra-complexterms based on the force field were thus avoided.

To demonstrate the transferability of the scoring function, the PepScopemethod has been applied to four physically different HLA receptors asdescribed in the examples section of this application. A systematicstudy of all natural amino acid substitutions at the anchor positions inHLA-A*0101 (A1), HLA-A*0201 (A2), HLA-A*2402 (A24) and HLA-B*0702 (B7)was performed. The predictions were compared with experimental dataeither from literature (A1 and B7) or from own binding assays (A2 andA24). The binding capacity of 39 nonamers FxSKQYMTx (x is any of the 20natural amino acids) was tested on A2 and A24. The latter data areunique in that they display the anchor specificity over the entire rangeof possible substitutions. This has enabled the inventors to quantifynot only favorable but also disruptive effects. With respect to thelatter, the inventors emphasize the generally underestimated roles ofdesolvation and conformational strain.

The PepScope method uses only structural data and a standard force fieldfunction to score anchor residues in HLA complexes. This approach offersseveral advantages: (i) its independence from experimental binding dataguarantees unbiased analysis with a greater sensitivity, (ii) the methodis equally well applicable in cases where experimental information isscarce, and (iii) computed affinities can be thoroughly rationalizedeither by dissection into physical contributions or by structuralinspection. Furthermore, since the method does not contain a trainingstep, it is characterized by a great transferability. The inventors havedemonstrated this by validation on four HLA receptors with divergentphysical properties.

Transferability of the method was demonstrated by its application to thehydrophobic HLA-A2 and -A24 receptors, the polar HLA-A1 and thesterically ruled HLA-B7 receptor. A combined theoretical andexperimental study on 39 anchor substitutions in FxSKQYMTx/HLA-A2 and-A24 complexes indicated a prediction accuracy of about ⅔ of a log-unitin Kd. Analysis of free energy contributions identified a great role ofdesolvation and conformational strain effects in establishing a givenspecificity profile.

Interestingly, the method rightly predicted that most anchor profilesare less specific than so far assumed. This suggests that many potentialT-cell epitopes could be missed with current prediction methods. Theresults presented in this work may therefore significantly affect T-cellepitope discovery programs applied in the field of peptide vaccinedevelopment.

The PepScope method has an original approach to quantify desolvationeffects. This is accomplished by in silico submersion of amino acidmodel compounds in explicit water, followed by standard energyminimization and proper rotameric sampling, energy calculation,averaging and selection. Combination of solvent terms with intra-complexenergies results in scores devoid of systematic errors. Thus,desolvation energies derived by the said in silico method are compatiblewith intra-complex terms.

The PepScope scoring function basically consists of three energeticcomponents: desolvation, direct ligand/receptor interactions andintra-complex strain (whereby the two latter components are two types ofintra-complex energy). These terms are strongly affected by localconditions in a complex, the latter forming the basis of specificity. Inorder for a residue to be contributive, its local interactions have tocompensate for unfavorable desolvation and strain. The net balance canbe very delicate, especially when aromatic or polar side-chains areinvolved. From a modeling point of view, this imposes very high demandson the accuracy of complex models. The inventors have demonstrated thatsuch high level of accuracy is attainable for buried anchors. It isobvious for the man skilled in the art that scoring of non-anchorresidues or full peptide sequences lies in the extension of themethodology of the present invention. The PepScope method is thereforeto be seen as an affinity scoring method for complete ligands.

Affinity Scoring Algorithm

Affinity scoring of anchor residues is accomplished basically by acombined side-chain rotameric search and energy refinement approach. Thefollowing steps are performed individually for all amino acidside-chains at each anchor position in all pA1a/HLA complex models.

The side-chain is introduced in standard geometry. Then, all rotamersfrom the same library as used in the model preparation are appliedconsecutively to the mutated side-chain. Dummy rotamers are used forGly, Ala and Pro. Each rotameric variant is submitted to 800 stepsconjugated gradient energy minimization. Moderate positional restraints(1 kcal/Å²) are applied to the full backbone of the complex except thesubstituted residue and its flanking peptidic groups. The cutoff fornon-bonded interactions is set to 14 Å. The following analyses arecarried out on the minimized structures: computation of accessiblesurface area (ASA) of the side-chain rotamer, conversion of ASA intopercentage buried surface area (% BSA), calculation of desolvationenergy (E_(desolv)), computation of side-chain/receptor non-bondedinteractions (E_(inter)) and computation of conformational strain energy(E_(strain)). The modeling step is concluded by selecting the rotamer rwith the best total energy score (E_(total)) in accordance with Eq. 1.

E _(total)=min_(r) {E _(desolv)(r)+E _(inter)(r)+E _(strain)(r)}  (1)

Affinity scores are calculated for all possible amino acid substitutionsat the anchor positions P2 and P9 in pA1a/A2, -A24 and -B7, andpositions P3 and P9 in pA1a/A1. Noise effects due to imperfections inindividual models or fluctuations in the minimization path are reducedby taking the average E_(total) value over three models constructed perreceptor type.

The PepScope scoring function by default subdivides each of the threeglobal energy terms into more elementary components, primarily for thesake of comprehensibility. In the case of the desolvation terms, thisalso enables working with functional group solvation parameters (GSPs)rendering desolvation terms conformation sensitive. The global energiesare subdivided according to Eqs. 2-4; the contributions are discussed inthe next paragraphs.

E _(desolv)=−Σ_(i)%BSA(i)×GSP(i)  (2)

E _(inter)=Σ_(i) {E _(vdw)(i)+E _(hbo)(i)+E _(ele)(i)}  (3)

E _(strain) =E _(rec) +E _(pep) E _(self)  (4)

In Eqs. 2 and 3, i denotes one of the chemical functions present in themutated side-chain (defined in the legend to FIG. 1). Any givenside-chain type is described by maximally three chemical groups. Allside-chain types comprise group 1, defined as the aliphatic moiety. Thesubdivision of E_(desolv) and E_(inter) into group contributions isjustified in view of the additive nature of surface areas and nonbondedenergy terms.

An important aspect of PepScope is that GSP values are derived de novo,i.e. from simulations of amino acid reference conformations in aspherical water box filled with explicit water molecules. This approachis expected to yield E_(desolv) energies that are maximally consistentwith the intra-complex terms E_(inter) and E_(strain), because bothtypes of energy are based on the same parameters and equations from theCHARMM force field. The PepScope solvent model is therefore designated“internal” as opposed to “external” models based on experimental groupor atomic solvation parameters.

The direct side-chain/receptor interactions (E_(inter)) are the mostobvious contributions. They consist of van der Waals interactionsquantified by a “6-12” Lennard-Jones potential (E_(vdw)(i)), H-bondsrepresented by a “10-12” potential (E_(hbo)(i)) and electrostaticinteractions calculated by a Coulombic equation with adistance-dependent dielectric constant (E_(ele)(i)). Van der Waals andH-bond interactions are computed with a cutoff distance of 16 Å while 25Å is used for electrostatic energy. Computed values for E_(vdw)(i),where i refers to aromatic or guanidinium groups, are reduced to 80% ofthe original value to avoid overprediction.

The strain term E_(strain) stands for “every increment in energy due tothe mutant side-chain, except direct interactions”. Computing it bycomparing energies before and after minimization would mostly result in“negative increments” due to global structural drift independent of thesubstitution. Therefore, strain contributions are computed on themutated and minimized structure and compared with the same terms derivedfrom the minimized pA1a/HLA structure (“mutant” strain minus “Ala”strain). A first component of E_(strain) is E_(rec), the strain energyresiding in the receptor, more precisely within the set of atoms closerthan 15 Å from the C_(β)-atom of the mutated position. A secondcomponent is E_(pep), the strain felt by the entire poly-Ala peptide(i.e., the full peptide minus the substituted residue). This termincludes both the self energy of the peptide and its interactions withthe receptor. The third component is E_(self), the self tension of themutation, including all bonded and nonbonded energies within the mutatedresidue (side-chain, main-chain and flanking peptide groups). Incontrast to E_(rec) and E_(pep), E_(self) is measured relative to theself energy of the same amino acid in the water box, and not relative tothe minimized pA1a/HLA structure. Occasionally, one or more of thestrain terms assume unrealistically high values. However, rather thanimposing general strain maxima, the full score is always calculatedfirst (Eq. 1) and then, if higher, truncated at 3.0 kcal/mol.

Computation of Group Solvation Parameters.

Acetylated and aminomethylated amino acids are placed at the center of aspherical water box with a radius of 37 Å and containing 6840 moleculesin a TIP4P configuration. Side-chain conformations are retrieved fromthe same rotamer library as used in the preparation of complex models.Rotamers are considered one by one, for all 20 natural residue types.For each rotamer, the dimensions of the system are reduced by retainingonly the water molecules in a 20 Å layer around the solute. Next, alloverlapping water molecules are removed. Overlap is defined as adistance between any solute-water atom pair smaller than the sum oftheir respective van der Waals radii, minus a tolerance of 1 Å. Thesystem is subsequently energy minimized by performing 200 stepsconjugated gradient minimization using a 14 Å cutoff and 10 kcal/Å²positional restraint on the water oxygen atoms (not on the hydrogens,nor on the solute atoms). The whole procedure is repeated 100 times perrotamer using slightly different initial placements (a uniform randomoffset relative to the center of the sphere was applied to the X-, Y-and Z-coordinates of the solute, sampled from the interval −2,+2 Å). Foreach rotamer, the 25 solutions having the best total side-chain/waterinteraction are retained and their values are averaged. Finally, therotamer with the lowest average energy is retained for each residuetype. FIG. 1 shows the computed energies, subdivided into van der Waals,H-bonding and electrostatic interactions for each chemical group.

Development of an Internal Desolvation Model

The energetic cost associated with the liberation of a ligand from asolvent environment is very large in comparison with the net gain infree energy upon binding to a receptor. Hence, the model used toquantify desolvation effects is critical for success. The known anchorpreferences of different class I receptor subtypes are describedreasonably well by the CHARMM force field but only for hydrophobic andnot for polar residues. Dissection of intra-complex interaction energiesinto contributions from separate functional groups shows that theinteractions from polar groups and, to a lesser extent, also aromaticatoms are much too strong to be correlating with experimentalaffinities. The interaction energies that are to be compensated by thesolvent model are typically around 20 kcal/mol for buried polarside-chains. Since this value is much larger than experimental transferfree energies from the vapor phase to water, one could question thecompatibility of such data with the force field used in the presentinvention. The inventors therefore analyzed solute-solvent interactionsof the 20 natural amino acid residues, in silico submerged into a waterbox. The energetic analysis was performed separately for van der Waals,H-bonding and electrostatic contributions and for the different chemicalfunctions present in the side-chain, using the CHARMM force field.

FIG. 1 shows that the aliphatic residues Ala, Pro, Val, Ile and Leu havesmall, negative hydration energies arising almost purely from van derWaals interactions. They remain below 5 kcal/mol in absolute value. Incontrast, polar side-chains other than Ser and Thr make about 3 to 4times stronger interactions, roughly between −13 kcal/mol (Lys) and −19kcal/mol (Glu). Thus, even nonpolar side-chains have favorable hydrationenergies but the latter are much smaller than those of polarside-chains.

All OH groups (Ser, Thr, Tyr) have similar values: negligible van derWaals and about −3 kcal/mol H-bonding and electrostatic interactions.

Amide and carboxylate groups have nearly identical H-bonding andelectrostatic interactions (about −5 and −7 kcal/mol, respectively).This may seem surprising in view of the carboxylate groups carrying anet charge, as opposed to amide groups. Apparently, the OC- and the twoHN-dipoles in an amide group are almost equivalent to the two OC-dipolescarrying negative charges in carboxylates. Compared to OH-groups, amidesand carboxylates have much stronger van der Waals (˜7×), slightlystronger H-bonds (˜1.5×) and much stronger electrostatics (˜2.5×). Verysimilar results are obtained for the joint N_(δ)-/N_(∈)H-groups from theHis imidazole ring. Considering that these contributions to hydrationenergy must be overcompensated in a complex in order for thecorresponding groups to have a net stabilizing effect, the valuesobtained are very large.

The charged amine of Lys and the guanidinium function of Arg both haveH-bonding terms similar to that of an OH-group (˜−3.0 kcal/mol). Alsothe electrostatic terms hardly differ: that of the guanidinium group is˜1 kcal/mol smaller than for OH. This is yet another unexpected resultin view of both Lys and Arg being charged and possessing much moredipoles than OH-containing residues. The main difference withOH-functions is observed at the level of van der Waals interactions: theLys and Arg polar groups respectively have 2.5 and 12-fold stronger vander Waals interactions than OH.

The S-/SH-groups of Cys and Met are poor H-bond formers and make weakelectrostatic interactions in water. In contrast, the van der Waalscontribution is exceptionally large (−3.3 kcal/mol), which is obviouslydue to the greater polarizability of S-atoms. Finally, aromatic atomshave a relatively simple hydration profile: no H-bonds, very weakelectrostatics and regular van der Waals interactions of about −1.3kcal/mol per heavy atom.

These computed hydration energies are to be considered as groupsolvation parameters (GSPs). The energetic cost associated with(partial) dehydration of a side-chain i consisting of functional groupsj, E_(dehyd), can be computed by Eq. 5 (analogous to Eq. 2):

E _(dehyd)(i)=−Σ_(j) GSP(i,j)f _(b)(i,j)  (5)

where f_(b)(i,j) is the fraction of buried surface area for group j ofside-chain i, calculated as

f _(b)(i,j)=(A _(r)(i,j)−A _(c)(i,j)/A _(r)(i,j)  (6)

and where A_(r)(i,j) and A_(c)(i,j) are the group ASAs in the referencestate (here, the rotamer selected in the water box simulations) and in acomplex, respectively.

Affinity Scoring Issues

Accurate modeling of the conformation of anchor residues inpeptide/receptor complexes is not very complicated, provided that modelsare prepared with state-of-the-art methodology, that the conformationalspace of the anchor side-chains is thoroughly explored, and that thestructures are adequately refined using appropriate restraints. Agreater problem however is to derive affinities from structural data. Itis a real challenge to develop a robust, generally applicable scoringmethod that does not require reparameterization for different systems,i.e. a transferable method.

The invention has contributed to the affinity scoring problem in severalways.

In one embodiment, the inventors have demonstrated that a standard forcefield is useful as the basis of a scoring function, provided that it issupplemented with a suitable solvent function.

In another embodiment, a method was developed to derive desolvationenergies on the basis of the same force field that is used to evaluateintra-complex energetic contributions. The hypothesis that such“internal” model would be preferred over “external” approaches becauseof a higher consistency in the calculated energies, is confirmed by theresults.

Yet another embodiment of the present invention applies a fully additiveenergy model, wherein diverse contributions from van der Waalsinteractions, H-bonds, electrostatics, and different types of bondedenergy are calculated separately and then simply added up in order toobtain final scores. As a matter of fact, there are two aspects to theproblem of (non-)additivity: (i) the question whether a simplepairwise-additive force field function is of sufficient accuracy, and(ii) in the case of multi-residue peptide ligands, the assumption thateach residue contributes independently to total affinity. Concerning thefirst type of non-additivity it is likely that the errors made by apairwise atomic force field function are not significant enough to urgefor a more sophisticated approach. Regarding the assumption ofindependence between different parts of a ligand, this is circumventedin the present invention by focussing on the peptide/MHC anchorresidues, the backbone of which is held in a stiff grip by the receptor,and the side-chains of which are relatively isolated. This way, theaffinity scoring problem could be studied without interference fromglobal, non-linear packing problems.

Another embodiment of the present invention demonstrates that the netligand binding free energy is fundamentally a delicate balance betweenvery large components: free energy of desolvation and free energy ofbinding. When decomposing the latter into direct ligand/receptorinteractions and induced strain, one arrives at two positive and onenegative contribution. FIG. 2 illustrates their mutual proportions,computed for all amino acid side-chains at position P2 in HLA-A2. Giventhe good correlation with experiment (Tables I and II), the individualterms should be meaningful. However, the implications from the point ofview of a modeler are, to put it mildly, enormous. The net bindingenergy of an “average” side-chain in a “typical” receptor pocket amountsat best about 25%, but mostly around 10%, or less, of the correspondingintra-complex interaction terms. This means that a 10% error in any ofthe independent contributions is likely to destroy the correlation withexperiment. Failure to simulate a single, relevant H-bond (or thegeneration of a false H-bond) distorts the computed interaction energyby 1.5 kcal/mol in H-bonding and roughly an equal amount inelectrostatic energy; this is equivalent to more than 1 log-order in Kd.

The present invention focuses on anchor residues, of which theconformation is strongly imposed by the receptor. In contrast,full-length (octa- to decameric) HLA class I binding peptides arecharacterized by a significant degree of flexibility near the middlepart. Scoring of full peptide sequences is possible on the basis of thescoring function of the present invention. Non-anchor residues are ingeneral contributing weakly to total affinity. Exposed residues remainlargely hydrated but cannot benefit from strong interactions. Buriedside-chains are more difficult: by definition, they pay the fulldehydration price but they are usually located in regions of loweratomic density, which leads to reduced (van der Waals) interactions.Assuming that they can bind free of strain, the interactions andhydration energies of polar side-chains are, as a rule of thumb, similarin size. For aromatic and aliphatic side-chains there usually remains asmall net profit. This explains the overall slight preference for apolarresidues at buried non-anchor positions (mostly P3 and P7). Thus, buriedpolar side-chains in low-density regions generally stand no chanceagainst non-polar ones, unless they are further stabilized by favorableelectrostatics or H-bonds, potentially also mediated by structured watermolecules. The technical complexity of modeling and scoring non-anchorresidues is therefore very high, but fundamentally the same rules apply.The present invention on conformationally more restrained anchorresidues can therefore be of great help in quantifying the contributionsthat are relevant to total affinity.

EXAMPLES

To demonstrate the transferability of the scoring function, the PepScopemethod has been applied to four physically different HLA receptors asdescribed in the examples section of this application. A systematicstudy of all natural amino acid substitutions at the anchor positions inHLA-A*0101 (A1), HLA-A*0201 (A2), HLA-A*2402 (A24) and HLA-B*0702 (B7)was performed. The predictions were compared with experimental dataeither from literature (A1 and B7) or from own binding assays (A2 andA24). The binding capacity of 39 nonamers FxSKQYMTx (x is any of the 20natural amino acids) was experimentally tested on A2 and A24. The latterdata are unique in that they display the anchor specificity over theentire range of possible substitutions. This has enabled the inventorsto quantify not only favorable but also disruptive effects. With respectto the latter, the inventors emphasize the generally underestimatedroles of desolvation and conformational strain.

The binding specificity of peptide/HLA complexes is strongly determinedby peptide anchor positions. Yet, the anchor preferences for most HLAsubtypes remain poorly characterized. The inventors showed that manycommonly accepted profiles are skewed in the sense that they containfalse positive and, especially, false negative information. The bindingcapacities of Trp and Met are generally undervalued. Small, polarside-chains (e.g. Ser, Thr) are usually assigned neutral values in Bimasand Syfpeithi scoring matrices while in reality their binding strengthis highly variable: relatively strong at A2-P2, A2-P9, A1-P2 and A1-P9but prohibitive at A24-P9, B7-P2 and B7-P9. In general, all currentbinding motifs and profiles have difficulties in correctly appreciatinganchor residues with intermediate binding capacity. Another observationis that true binding profiles are usually broader, i.e. less specific,than assumed thus far.

Example 1 Model Preparation

The preparation of model complexes depends on the availability oftemplate structures in the Protein Data Bank. If one or more tertiarystructures for a given HLA subtype are known, a selection is madeprimarily on the basis of crystallographic resolution. Additionalcriteria such as R-factor, length of the bound peptide, experimentalaffinity of the latter, width of the binding groove, etc., areconsidered as well. For A2 1DUZ was selected as the starting template.Since no exact templates were available for A1, A24 and B7, these had tobe modeled. First, a structure was selected from the PDB using the samecriteria as for A2, considering also the sequence similarity of receptorresidues in contact with the peptide. The following template structureswere chosen: 1HSB (type Aw68) for A1, 1DUZ (type A2) for A24 and 1A9E(type B35) for B7. The coordinates for amino acid residues 1-181 (theα₁α₂-domain) and the extant peptide were extracted from the files. Watermolecules were ignored in this study. The truncated peptide/HLAcomplexes were submitted to 200 steps unrestrained steepest descentenergy minimization.

In a next step, the substitutions required to construct a desired HLAsequence were introduced and modeled by the FASTER algorithm asdescribed in pending patent application WO01/33438 which is incorporatedherein by reference, and as described by Desmet et al. (Proteins, 2002,ref 40). Briefly, the peptide was temporarily removed from the complexand the mutations were performed in standard geometry. The FASTERalgorithm was then applied to search for the best global packingarrangement of the substituted side-chains, using a rotamer librarycontaining 899 conformational states for the 20 natural amino acidside-chains. The structure with the best global conformational energywas selected and refined by an extra 200 steps unrestrained steepestdescent energy minimization. The original peptide was finally placedback as poly-Ala with the original backbone coordinates (likewise forthe 1HSB-based model containing only the first three and last twopeptide residues). The resulting model was stored as input data for thedocking algorithm. The homology modeling step was skipped for HLA-A2.

In a further step, a high affinity peptide was docked into each HLAmodel by means of a flexible peptide docking algorithm, as described inDesmet et al (FASEB J). Briefly, the poly-Ala peptide was replaced bythe sequence YTAVVPLVY for A1, FLSKQYMTL for A2 and A24, and FPVRPQVPLfor B7. Bond lengths and angles, also for the main-chain, wereinitialized in standard geometry to avoid structural bias. The dockingalgorithm was then instructed to rebuild the peptide from the N-towardsthe C-terminus Limited translation (max. 1 Å) was allowed. The samebackbone-dependent rotamer library was used as before. All grooveside-chains with rotatable bonds were kept flexible during the docking.Typically, the docking algorithm identifies 50 to a few hundreds ofenergetically favorable complex structures. For the present study onanchor residues, only three structures per receptor type were needed.They were selected from the top-10 docking solutions, consideringaspects like general packing quality, peptide backbone variation andreceptor side-chain conformation. The selected complex structures werethoroughly energy minimized by 800 steps unrestrained conjugatedgradient minimization. Finally, all peptides were again mutated intopoly-Ala. The resulting structures, referred to as pA1a/HLA models, werestored as input data for the scoring algorithm.

Example 2 Peptide Binding Assays

IC₅₀ values were determined using a cell-based assay, largely accordingto van der Burg et al, Hum Immunol 1995; 44:189-98 and Kessler et al,Hum Immunol 2003; 64:245-55. Briefly, immortalized B-cells displayingHLA-A*0201 or HLA-A*2402 homozygously (VOSE EBV (A*0201, B*4402,Cw*0501/0711) and HATT EBV (A*2402, B*4801, Cw*0801/1202) are strippedof their self peptides, followed by equilibrium binding of test peptidein competition with fluorescent reference peptide(FLPSDC(5Fluorescein)FPSV for A2 and RYLKC(5Fluorescein)QQLL for A24). A10-point concentration range of test peptide is used for eachmeasurement, typically in 2-fold increments from 62.5 nM to 32 μM, in aconstant background of 30 nM reference peptide. Adapted ranges were usedfor excellent binders (minimal concentration 7.8 nM) and weak binders(maximal concentration 128 μM). 50% inhibitory concentrations(IC₅₀-values) were calculated as averages obtained from at least 3independent measurements, i.e. from different cell preparations andpeptide dilutions. Test peptides >95% pure (Thermo Electron GmbH) werestored at 10 mM in DMSO at −20° C. Cysteine-containing peptides werestored at 10 mM in 1 mM DTT DMSO. DTT did not affect binding ofFLSKQYMTL control peptide but significantly improved binding andreproducibility for FCSKQYMTL and FLSKQYMTC on both A2 and A24 (notshown). IC₅₀-values were converted to binding free energies (AG) usingthe relationships

Kd=IC ₅₀ Kd _(ref) /c _(ref)  (7)

and

AG=RT ln(Kd)  (8)

where Kd_(ref) and c_(ref) are the dissociation constant and formalconcentration of the reference peptide, respectively. The Kd_(ref)values were derived from an independent binding assay in which thefluorescence intensity was measured as a function of increasingconcentrations of reference peptide. Non-linear curve fitting using asingle-site binding scheme gave approximate Kd's of 3 nM for the A2 and30 nM for the A24 reference peptides, respectively.

Example 3 Affinity Scoring of HLA-A2 Complexes

HLA-A*0201 (A2) is one of the most extensively studied peptide bindingreceptor molecules. It is known to show a strong preference for peptideligands having Leu at position P2 and Val or Leu at P9. The modeling ofall 20 natural amino acid residues at the anchor positions P2 and P9 wasperformed systematically for the three pA1a-A2 models. FIG. 2 shows theaveraged scoring values for position P2 in pA1a-A2. Total affinityscores have been dissected into the three major energetic components:desolvation energy, side-chain/complex interaction (includinginteractions with all pA1a residues but the mutated one) and “localstrain” (including strain within the mutant residue). All values areexpressed in units of kcal/mol, i.e. the units of the force fieldequations.

A striking observation from FIG. 2 is that direct side-chain/complexinteractions are very large: for 50% of the residues they lie in therange −15 to −20 kcal/mol. It is seen that most of the side-chains“larger than Leu” have similar interactions with values around −17kcal/mol. The smaller side-chains, most of which are aliphatic, havevariable interactions ranging from 0 (Gly) to −12 kcal/mol (Leu). Thus,in absence of any compensatory effects, pure force field derivedenergies would predict aromatic and polar side-chains to be largelypreferred over aliphatic residues at position P2 in A2 complexes.

Rather, the opposite is true: peptides binding strongly to A2 mostlyhave Leu, Met, Ile and sometimes Val, Ala or Thr at position P2. FIG. 2shows that the explanation is to be found at two levels, i.e. thecompensatory desolvation and strain terms. From FIG. 1 it followed thatpolar and aromatic side-chains are characterized by relatively largedesolvation energies. The latter appears to be the primary reason forSer, Asn, Asp and Glu to be undesired. Yet, the prohibitive nature ofvarious other side-chains can be explained only by an additionalcompensatory component. Here, this is found to be the strain induced inthe receptor by ill-fitting side-chains. The latter is applicable mainlyto the aromatic Phe, Tyr, Trp, His and the basic Lys and Argside-chains. Even the aliphatic Val, Ile, Leu and, especially, Proinduce some tension in their environment.

Gln is the sole polar side-chain that is reasonably well accepted in thereceptor pocket at P2. FIG. 2 shows that Gln can be accommodated almostfree of strain, but the desolvation cost is large (17.2 kcal/mol). Yet,Gln seems to have no problem in compensating for this by makingexceptionally favorable interactions. FIG. 2B shows more detail. Thealiphatic part of its side-chain interacts favorably with the pocketresidues, almost exclusively through van der Waals terms. The amidegroup contributes even better: −9.6 (2V), −2.6 (2H) and −2.9 (2E)kcal/mol from van der Waals, H-bonds and electrostatic interactions. Thetotal van der Waals interaction in the complex is −15.7 kcal/mol orroughly three times stronger than in the water box. In itself this isnot an exceptional situation for buried, well-packed side-chains,including the polar ones. What really makes the difference is theability of Gln to form two nearly ideal H-bonds, one between the firstof its two amide H_(∈) atoms and Glu A63.0_(∈) and the other between thesecond amide H_(∈) and the backbone A63.O atom. Only Lys can make thesame two H-bonds, but this occurs at the cost of a considerable strainof 5 kcal/mol distributed in proportions of 2:1:2 over side-chain self,peptide pA1a energy, and receptor tension, respectively. Csh (cystein),Ser, Thr and Asn can only make 1 H-bond, which is apparently notsufficient to make them preferred residues.

The net result of the delicate balance is shown by the tiny black barsin FIG. 2. When plotted on a scale suitable to display the individualcontributions, the total energy score almost vanishes (the total energyoften amounts to less than 10% of the direct side-chain/receptorinteraction). Pending the experimental validation below, the top-10ranked residues (L-M-V-I-Q-A-T-S-C-K) correlate well with those of thepopular Bimas scoring matrix, (L-M-I-Q-V-E-A-T-C-G). Only Glu at rank 6in the Bimas series is in disagreement with the method of the presentinvention ranking Glu as the fifth worst type at P2 in A2. It will beseen that Glu in the Bimas matrix is a false positive.

Experimental Validation for A2

Validation of the predicted binding capacity was achieved by directcomparison with experimental affinities obtained for a series ofsystematic P2 mutants of a selected peptide. The nonameric peptideFLSKQYMTL from Hepatitis B virus polymerase, residues 676-684, waschosen because of its cross-reactivity between A2 and A24, as predictedby the full-length peptide scoring method which is part of the EpiBase™platform.

Since experimental binding free energies were obtained for full-lengthpeptides and expressed in kJ/mol, while predicted scores are for singleresidues in kcal/mol, a calibration step was required. Raw predictedscores (E) were converted to calibrated scores (E′) by the equation

E′(kJ/mol)=a+b E(kcal/mol)  (9)

where a is the intercept and b the slope of the least squares regressionline of the correlation plot. For the P2 mutants, the slope of theregression line (b) equaled 2.44 kJ/kcal and the intercept was −33.3kJ/mol. The latter value is a measure for the affinity contributed bythe peptide minus the substituted side-chain.

Table I shows the data for the wild type peptide FLSKQYMTL and all 38 P2and P9 substitutions. The experimental data confirm the potential of Leuat P2 for strong binding. The Met and Ile variants essentially bind withthe same affinity. Val and Gln at P2 also allow strong binding. Thesedata are greatly consistent with what is known about the P2 preferenceof A2 binding peptides. However, the dataset further shows that Thr,Ala, Gly and Ser are feasible residues as well, something which israrely recognized. Even the Csh and Phe variants have a dissociationconstant that is less than 1 log-order worse than the wild type (1logKd=5.74 kJ/mol). Only Tyr, Arg, Lys, Glu, Pro, Asp and Trp are trulydisruptive (logKd more than two units higher than wt).

TABLE I Predicted vs Experimental Affinities of FLSKQYMTL^(a) Mutants A2A24 P2 P9 P2 P9 AA G_(exp) Score G_(pr) Error AA G_(exp) Score G_(pr)Error AA G_(exp) Score G_(pr) Error AA G_(exp) Score G_(pr) Error L−42.1 −4.8 −45.1 −2.9  M −42.7 −3.3 −40.5 2.2 W −47.0 −6.2 −47.2 −0.1 W−44.4 −4.5 −40.1  4.3 M −41.6 −4.4 −44.1 −2.5  V −42.2 −4.2 −41.8 0.4 F−45.2 −4.5 −43.8  1.4 F −44.4 −4.2 −39.0  5.5 I −41.3 −2.7 −40.0 1.3 L−42.1 −3.3 −40.5 1.6 Y −44.7 −5.3 −45.4 −0.7 I −41.4 −4.2 −39.0  2.4 V−40.5 −3.2 −41.1 −0.5  A −41.8 −2.0 −38.8 3.0 M −44.3 −4.0 −42.8  1.5 L−39.7 −4.0 −38.2  1.5 Q −40.1 −2.4 −39.2 1.0 I −41.3 −2.5 −39.4 2.0 Q−43.2 −1.2 −36.9  6.2 M −36.7 −4.1 −38.6 −1.9 T −39.7 −1.4 −36.6 3.0 T−40.2 −4.4 −42.2 −1.9  A −42.1 −1.7 −37.9  4.2 V −32.6 −2.3 −32.1  0.5 A−39.5 −2.1 −38.4 1.0 F −40.2 0.1 −35.7 4.5 G −39.8 0.1 −34.3  5.5 Y−32.4 −1.6 −29.5  2.9 G −39.3 0.5 −32.2 7.0 S −38.8 −1.8 −38.3 0.4 L−39.7 −3.9 −42.5 −2.8 H −30.8 1.0 −20.0 10.8 S −38.6 −0.5 −34.5 4.1 C−38.7 −0.6 −36.7 1.9 I −39.4 −3.4 −41.4 −1.9 C −27.4 −1.1 −27.8 −0.4 C−37.1 −0.5 −34.5 2.6 G −36.9 −0.3 −36.3 0.6 V −38.6 −3.1 −40.9 −2.3 A−26.9 −1.6 −29.6 −2.8 F −36.6 0.8 −31.4 5.2 W −36.5 3.0 −31.6 5.0 T−37.9 −1.2 −36.9  1.0 G −24.6 0.3 −22.4  2.2 N −35.7 0.5 −32.0 3.7 Q−35.8 −1.5 −38.0 −2.2  S −36.7 −0.3 −35.1  1.7 Q −18.6 0.7 −21.3 −2.6 H−33.8 0.1 −33.1 0.7 P −35.1 −0.3 −36.3 −1.1  H −34.8 0.1 −34.2  0.6 N−18.4 3.0 −12.7  5.7 Y −29.7 2.2 −28.0 1.6 H −34.9 0.4 −35.3 −0.4  C−33.1 −0.7 −35.9 −2.9 T −18.4 −0.9 −27.0 −8.6 R −27.6 2.4 −27.4 0.2 N−34.1 0.3 −35.4 −1.4  N −32.7 1.8 −30.6  2.1 S −18.3 −0.7 −26.3 −7.9 K−27.0 0.1 −33.2 −6.2  Y −33.7 3.0 −31.6 2.2 R −31.9 1.0 −32.4 −0.4 K−18.3 −0.4 −25.0 −6.7 E −26.5 1.8 −28.9 −2.5  K −30.3 −1.6 −38.1 −7.8  P−30.0 0.3 −33.9 −3.8 R −18.2 0.5 −21.9 −3.7 P −26.1 0.2 −32.9 −6.9  E−30.0 3.0 −31.6 −1.6  E −29.7 1.8 −30.6 −0.9 P −18.2 1.3 −19.1 −0.9 D−23.8 2.5 −27.2 −3.4  D −29.8 3.0 −31.6 −1.8  K −29.0 0.0 −34.4 −5.4 E−17.4 0.4 −22.1 −4.7 W −23.0 1.5 −29.6 −6.7  R −28.1 1.5 −33.8 −5.7  D−28.8 1.3 −31.7 −2.9 D −17.2 3.0 −12.7  4.5 σ: σ: 3.1 σ: 3.1 σ: 5.0 3.9AA, amino acid placed at the indicated anchor position (P2/P9,) of thepeptide FLSKQYMTL in the indicated complex (A2/A24); ΔG_(exp),experimental binding free energy in kJ/mol; Score, uncalibrated affinityscore in kcal/mol; ΔG_(pr), calibrated affinity in kJ/mol; Error,difference between ΔG_(exp) and ΔG_(pr). Values <−3 kJ/mol are given inbold; values >3 kJ/mol are underlined; σ, standard deviation of Errorvalues.

The predictions follow the observed trends well. The top-5 best rankedpeptides (Leu, Met, Val, Ile, Gln) exactly coincide with theexperimental top-5. The moderately binding Gly, Ser and Phe mutants areunderpredicted by approximately 1 logKd in magnitude. The opposite holdsfor the disruptive residues Lys, Pro and Trp although the predictedvalues are certainly not suggestive of favorable interaction. All otherpredictions are correct to within about 0.5 logKd. The overall standarddeviation of the difference between predicted and experimental bindingenergy is 3.9 kJ/mol (−2/3 logKd).

The same theoretical and experimental analysis was performed on thesecond anchor position P9. Here again, the best peptides contained thewell-known motif residues Val, Leu or Met. More surprising was theobservation that Ala, Ile, Thr, Phe, Ser and Csh substitutions caused areduction in affinity compared to wt of less than ˜0.5 logKd. EspeciallyPhe and Ser are usually considered prohibitive. Further, Gly and Trp arealso presumed to be disruptive, but they do fall within the 1 logKdrange from wt. Truly prohibited at P9 (wt+2 logKd) are only the chargedresidues Lys, Glu, Asp and Arg.

The P9 predictions are generally of the same or even slightly betterquality than for P2. Only the value for Lys is predicted with an errorlarger than 1 logKd (in the false positive sense). A similar yet smallererror is observed for Arg. Underpredicted (in the false negative sense)are Trp and Phe which experienced a relatively high strain in the models(12.1 and 5.6 kcal/mol, equivalent to 29.5 and 13.7 kJ/mol,respectively; the models were probably not fully relaxed). All otherresidue types were predicted with an error less than ˜0.5 logKd. Theglobal standard error was 3.1 kJ/mol. Interestingly, many features thathave not been recognized before were correctly predicted: (i) Val is notthe best possible residue at P9, (ii) Met, Leu, Ala and Ile are almostequally preferred, (iii) 11 other residue types (50%) bind with lowerbut non-prohibitive strength. Analysis of the structural data showedthat all but the aromatic side-chains can be accommodated free of straininto a medium-sized, mainly hydrophobic pocket (the F pocket). In thecase of Phe and Trp, the computed strain was probably evenoverestimated. Moreover, Csh, Ser, Thr, Asn and Gln can form one idealH-bond. Though not enough for strong binding, this evidently helps tobroaden the specificity profile. The results are supported by thesystematic survey of Rudolf et al who checked the A2 affinity of allpossible 9-mers derived from HPV-18 E6 and E7 proteins. Fifteen out ofthe 247 peptides (6%) had a Kd below 1 μM but only 4 had Val at P9, 4other had Leu, 1 Ile, and 6 had a non-standard anchor residue. Inconclusion, the specificity profile at the anchor residues in A2 is wellrepresented by the predictions and much more permissive than so farassumed.

Example 4 Binding Specificity of A24

HLA-A*2402 (A24) is another abundant MHC class I receptor with similarhydrophobicity as A2 but a significantly different (presumed)specificity profile. The anchor positions also appear at positions P2and P9 but the consensus motif is P2(Y/F),P9(L/F/I/M) orP2(Y/F),P9(F/W/I/L). Both specificity pockets are considerably largerthan in A2. For the pocket at P2, this is mainly due to the Phe→Sermutation in the receptor at residue A9, while the Leu→Ala mutation atA81 can be held responsible for the larger F-pocket at the peptideC-terminus. The Asn at A77 (i.s.o. Asp) also tends to form a H-bond withTyr-A116, which rigidifies part of the pocket wall.

The predictions suggested two interesting possibilities, namely, (i)that there is an overall similarity between the anchor preferences of A2and A24, and (ii) that Trp is the most preferred residue at both anchorpositions. The latter is completely overlooked in the Bimas andSyfpeithi scoring matrices and partially in the motif analysis. Also,the idea that A24 would be compatible with the A2-motif, with additionalpreference for bulky aromatic anchors is not known. Therefore an A24binding assay was set up to test the same 39 P2 and P9 mutants ofFLSKQYMTL on this receptor.

The experimental data in Table I confirmed the expectations: the A2-P2preferred residues Met, Gln, (A1a), (Gly), Leu, Ile and Val and theA2-P9 preferred Ile, Leu and Met (not Val) have similar affinities forA24. Also the polar residues show the same disruptive character.Moreover, aromatic side-chains (with the exception of Tyr at P9) arecontributing very strongly to affinity, a feature that is specific toA24. Trp is the “winner” at both positions P2 and P9, which is inagreement with the predictions but not with the presumed A24 motif. Itis possible that the lower intrinsic amino acid frequence of Trp can beheld responsible for the gaps in motifs based on experimental data. Forthat matter, the same fact could also explain the often underestimatedpreference of Met at various positions in different receptors.

A number of deviations between theoretical and experimental data exist.The tiny side-chains Ala and Gly are underpredicted at P2 by about 1order of magnitude in Kd. This is often seen at other positions as well.A possible explanation could be the presence of structured watermolecules which are ignored in the simulations. The underpredicted Glnmight be explained in the same way. Structural analysis showed that Glnat P2 adopts an identical conformation as in A2, but its van der Waalsinteractions are reduced by ˜1 kcal/mol (equivalent to ˜2.5 kJ/mol), dueto the mutation Phe→Ser at receptor position A9. Given the constitutionof the pocket, it is likely that one or more water molecules furtherstabilize the free O_(∈) atom of Gln. After all, Gln is a well accepted(and commonly underestimated) residue at P2 in A24.

Position P9 in A24 is a difficult case. The pocket at P9 is very largebut not well shaped to accommodate bulky side-chains. Only Trp can takefull advantage of the Leu→Ala mutation at A81 but even here it suffersfrom a significant compensatory strain (ranging from 4.6 to 8.5 kcal/molin the three models). Phe experiences less strain but the latter is alsostrongly fluctuating (0.7 to 5.5 kcal/mol). All in all, both Trp and Phereceive similar scores, in agreement with experiment, but both aresomewhat underestimated presumably due to incomplete relaxation in thesimulations.

The P9 position in A24 is also special for another reason, namely itsenormous diversity in binding affinity. The experimental affinity rangespans 27 kJ/mol or ˜5 orders of magnitude in Kd. As much as 10 of the 20residue types (50%) cause very poor binding. The reason for thispronounced specificity is related to the weird shape of the F-pocket.Side-chains larger than Ala tend to bump into the relatively rigid wallformed by A77-Asn, A116-Tyr and A147-Trp. This causes a residual strainof 1 kcal/mol (˜2.5 kJ/mol) or higher. More important, however, is theloss of interaction with residue A81 (Ala i.s.o. Leu) for allside-chains that do not properly fill the pocket. Finally, the Alamutation leaves a fully hydrophobic hole that does not harbor structuredwater well. The combination of these three effects ensures that smalland/or polar side-chains are absolutely unwanted at P9 in A24. Thoughconceptually understood, the peculiarities of the P9 region severelycomplicate modeling, which explains why the predicted values fluctuatemore than they usually do. Nevertheless, the global standard errorremains acceptable (5.0 kJ/mol) and the top-5 predicted residues (Trp,Phe, Ile, Met and Leu) exactly coincide with the experimental top-5.

When pooled, the predicted affinities of all peptides tested on A2 andA24 correlate with an R² of 0.77 and a standard error of 3.85 kJ/mol(0.67 logKd). The only true outlier is His at A24-P9. Note that noexperimental information (apart from the calibration needed forconversion to free energy units) was used in the development of thispurely structure-based scoring method. The method is therefore totallyunbiased, the advantages of which show up in the discovery of previouslyunknown anchor preferences.

Example 5 Binding Specificity of B7

The inventors wanted to examine the performance of PepScope onHLA-B*0702 (B7), a receptor with a pronounced preference for Pro atposition P2 and a P9 specificity with shared features from A2 and A24.Especially the P2 profile seemed intriguing: according to a pointmutation analysis by Sidney et al. on the HIV nef 84-92 peptide(FPVRPQVPL), all P2 mutants were binding at least 100 times weaker thanthe native peptide sequence, thus only the wt Pro would be allowed atP2.

The inventors followed exactly the same approach as for A2 in thescoring of all possible amino acid side-chains at positions P2 and P9 inthree B7 models. Table II shows the averaged total scores for theside-chains in a pA1a/B7 context. These values were compared to those ofSidney et al and two frequently used scoring matrices, i.e. Bimas andSyfpeithi.

TABLE II HLA-B7 Anchor Specificity P2 P9 PepScope Sidney Bimas SyfpeithiPepScope Sidney Bimas Syfpeithi score Kd score score score Kd scorescore AA (kcal/mol) (nM) (a.u.) (a.u.) AA (kcal/mol) (nM) (a.u.) (a.u.)P −4.8    13 20 10 M −3.5 3.8 10 6 A −1.9 >1300 3 0 L −2.3 13.6 40 10 S−0.9 >1300 1 0 I −1.8 22.4 4 6 C −0.6 ND 1 0 F −1.7 9.5 0.2 6 V −0.5 ND5 0 V −1.1 23.8 2 6 T −0.5 >1300 1 0 A −0.7 62.3 1 6 G 0.0 >1300 1 0 H−0.3 238 0.1 0 M 0.7 ND 1 0 G 0.0 ND 0.1 0 N 0.8 >1300 1 0 W 0.0 345 0.20 I 1.2 ND 1 0 K 0.4 >380 0.1 0 L 1.2 >1300 1 0 Y 0.6 >380 0.2 0 E 1.5ND 0.1 0 R 0.7 ND 0.1 0 H 1.7 ND 0.1 0 S 0.9 ND 0.2 0 D 2.0 >1300 0.1 0T 1.0 >380 1 6 Q 2.0 >1300 1 0 Q 1.0 >380 0.1 0 W 2.1 ND 0.1 0 C1.2 >380 1 0 Y 2.5 ND 0.1 0 D 1.3 >380 0.1 0 F 3.0 >1300 0.1 0 P 1.4 ND0.1 0 R 3.0 ND 0.1 0 E 2.8 ND 0.1 0 K 3.0 >1300 0.1 0 N 3.0 >380 0.2 0AA, amino acid placed at the indicated anchor position (P2/P9) of thepeptide FPVRPQVPL in the B7 complex; PepScope score, uncalibratedaffinity score; Sidney Kd, dissociation constant published by Sidney etal.⁵⁵; Bimas score (prediction score⁵¹); Syfpeithi score (predictionscore⁵⁴); a.u., arbitrary units.

PepScope indeed came up with Pro as the elected P2 residue. The gap withthe second best (Ala) was about 3 kcal/mol, which is one of the largestdifferences the inventors ever observed. Yet, if the Sidney profilebased on a single peptide sequence were true in general, then the Alascore would still be somewhat overestimated. Analysis of the structuraldata indicated that the primary reason for the observed profile is thesteric repulsion of all side-chains larger than Gly, Ala, Pro and Ser.The Pro ring hydrocarbons fill the pocket in a strainless fashion andmake strong van der Waals interactions (−8.1 kcal/mol), so it only hasto compensate for desolvation. Ala can also bind free of strain butinteracts more weakly (−3.6 kcal/mol). Val makes identical van der Waalsinteractions as Pro (−8.2 kcal/mol) but induces 4.4 kcal/mol of strain;together with the desolvation term (3.7 kcal/mol) and the minorelectrostatics (−0.3 kcal/mol), the balance becomes slightly favorable(−0.5 kcal/mol). Unfortunately, Sidney et al. do not provide data forthe Val mutant but the Bimas matrix takes it up as a feasible residue.Several other side-chains can make relatively strong interactions butthey invariably pay a very high “strain” price for it (e.g. Leu). Ser,Thr and Asn are subject to a more delicate balance between differentenergetic contributions: in the three models they make 1-2 (Ser and Thr)or 2-3 (Asn) H-bonds, but again this is overcompensated by inducedstrain. If the latter were not the case, these residue types would haveeven been preferred. The question may be asked whether the computedstrain terms for these small residues are real, but the experimentaldata force to conclude affirmative.

Position P9 has totally different features. Its profile looks similar tothat of A2-P9 and A24-P9, with a strong preference for Met, Leu, Ile,Val and Phe. The relatively strong affinity for Phe (and even Trp), inspite of the absence of the receptor A81 Leu→Ala mutation, could beexplained by a tendency of the F-pocket to open up as a result of AspA114 attracting Tyr A116 by means of a H-bond. Another strikingobservation is that the top-ranked residue type, Met, is indeedidentified by Sidney et al as the most potent one. Note that neitherBimas nor Syfpeithi have correctly appraised the capacity of Met. Also,Phe is significantly misjudged by Bimas: the coefficient <1 in thescoring table suggests it is disfavored while it is actually one of thebest. Overall, it can be concluded that the scores of the presentinvention are in much better agreement with experimental affinities thanthe ranking by both Bimas and Syfpeithi.

Example 6 Binding Specificity of A1

Having examined the anchor specificity of two typically hydrophobic (A2and A24) and one sterically driven receptor (B7), the inventors decidedto test PepScope on a more polar system. HLA-A*0101 (A1) is such areceptor, showing marked preference for Asp and Glu at P3 (not P2) andTyr, Lys, Arg and Phe, at P9. Position P2 prefers small, polarside-chains like Thr and Ser but a pronounced motif has not beenidentified. Hydrophobic side-chains seem to play an inferior role ingeneral. The A1 system was therefore considered as an important testcase for the validation of the solvent model and the scoring of H-bondsand electrostatic interactions.

Since no studies on systematic anchor substitutions are available, thescores of this invention were compared with Bimas data (FIG. 3). Thefirst impression is that the results from both methods are in fairagreement. There is a consensus about strong binding capacity of Thr atP2, Asp and Glu at P3 and Tyr, Lys, Phe and Arg at P9. Both methods alsoagree about the prohibitive nature of Glu, His, Phe, Arg, Trp and Lys atP2, Arg and Lys at P3 and Pro, Asp and Glu at P9. However, somedeviations are observed as well. According to the computations, Tyr is apoor yet tolerated residue at P2, while Bimas considers it to beprohibitive. The peptide IYQYMDDLY has been identified as amedium-affinity binder, suggesting that Tyr at P2 is at least feasible.Met and Trp at P3 and P9 are ranked higher by PepScope than by Bimas.The latter is recurrently observed for most receptor systems, and issupported by direct affinity measurements. Finally, Thr received thefourth best score at P9 (−2.7 kcal/mol), similar to the known anchorsPhe and Arg, while Bimas assigns a neutral score (1.0). The inventorsknow of only one experimental example with Thr at P9, but it is quiteconvincing: the strongest A1-binding peptide from HPV-18 was found to beYSDSVYGDT, with a Kd of 188 nM.

The underlying structural reasons for the observed polar preferenceswere examined A common feature for Thr/Ser at P2, Asp/Glu at P3 andLys/Arg at P9 is their ability to bind relatively free of strain (thehighest tensions were ˜1.5 kcal/mol for Asp/Glu at P3 and 2.5 kcal/molfor Arg at P9). Thus, the interactions made by these residues in thecomplex are compensated only for desolvation energy (FIG. 1). TheOH-functions of both Ser and Thr at P2 can form two nearly ideal H-bonds(one as donor to Glu-A63 and one as acceptor from Asn-A66) for whichthey receive −2.4 kcal/mol. They also receive an equal (Ser) or slightlybetter (Thr: −2.9 kcal/mol) amount of electrostatic energy. Togetherwith the other interactions, the balance is slightly in favor of Thr(−2.6 kcal/mol), immediately followed by Ser (−2.0 kcal/mol). The totalscores for Ser and Thr are “good” though not “excellent”; another H-bondwould be needed to make them elected anchors.

Asp and Glu, in contrast, are true anchor residues at position P3. Aspcan form a double bifurcated H-bond with Arg-A156 (−3.1 kcal/mol) andreceives an extra −6.2 kcal/mol electrostatic energy, mainly from thesame Arg but also from the peptide backbone NH dipoles of residues P3and P5. Glu forms two suboptimal H-bonds with Arg-A156 and another weakH-bond with His-A70, for which it receives a total of −2.7 and −5.2kcal/mol in H-bonding and electrostatic energy, respectively.

Lys and Arg at P9 can both form a single H-bond with Asp-A116 (yielding−1.4 and −1.9 kcal/mol, respectively), but the electrostatics are onlyof moderate quality (−1.2 and −1.8 kcal/mol, respectively). However, Lyscan bind in a totally relaxed way, while Arg induces 2.5 kcal/mol ofstrain. The van der Waals interactions largely suffice to compensate fordesolvation, so that the end balance for both is relatively favorable(−4.2 and −2.6 kcal/mol, respectively). For comparison, Tyr has a totalenergy of −5.7 kcal/mol, a value that can rightly be associated with astrong anchor.

REFERENCES

-   1. Rosenfeld R, Zheng Q, Vajda S, DeLisi C. Flexible docking of    peptides to class I major-histocompatibility-complex receptors.    Genet Anal 1995; 12:1-21.-   2. Desmet J, Wilson I A, Joniau M, De Maeyer M, Lasters I.    Computation of the binding of fully flexible peptides to proteins    with flexible side chains. FASEB J 1997; 11:164-172.-   3. Dunbrack R L, Karplus M. Conformational analysis of the    backbone-dependent rotamer preferences of protein side-chains.    Nature Struct Biol 1994; 1:335-340.-   4. De Maeyer M, Desmet J, Lasters I. All in one: a highly detailed    rotamer library improves both accuracy and speed in the modelling of    sidechains by dead-end elimination. Fold Des 1997; 2:53-66.-   5. Schrauber H, Eisenhaber F, Argos P. Rotamers: to be or not to be?    An analysis of amino acid side-chain conformations in globular    proteins. J Mol Biol 1993; 230:592-612.-   6. Mendes J, Baptista A M, Carrondo M A, Soares C M. Improved    modeling of side-chains in proteins with rotamer-based methods: a    flexible rotamer model. Proteins 1999; 37:530-543.-   7. Madden D R, Garboczi D N, Wiley D C. The antigenic identity of    peptide-MHC complexes: a comparison of the conformations of five    viral peptides presented by HLA-A2. Cell 1993; 75:693-708.-   8. Chen Y, Sidney J, Southwood S, Cox A L, Sakaguchi K, Henderson R    A, Appella E, Hunt D F, Sette A, Engelhard V H. Naturally processed    peptides longer than nine amino acid residues bind to the class I    MHC molecule HLA-A2.1 with high affinity and in different    conformations. J Immunol 1994; 152:2874-2881.-   9. Batalia M A, Collins E J. Peptide binding by class I and class II    MHC molecules. Biopolymers 1997; 43:281-302.-   10. Persson K, Schneider G. Three-dimensional structures of MHC    class I-peptide complexes: implications for peptide recognition.    Arch Immunol Ther Exp 2000; 48:135-142.-   11. Guo H C, Jardetzky T S, Garrett T P, Lane W S, Strominger J L,    Wiley D C. Different length peptides bind to HLA-Aw68 similarly at    their ends but bulge out in the middle. Nature 1992; 360:364-366.-   12. Stern L J, Wiley D C. Antigenic peptide binding by class I and    class II histocompatibility proteins. Structure 1994; 2:245-251.-   13. Zhang W, Young A C M, Imarai M, Nathenson S G, Sacchettini J C.    Crystal structure of the major histocompatibility complex class I    H-2K^(b) molecule containing a single viral peptide: implications    for peptide binding and T-cell receptor recognition. Proc Natl Acad    Sci USA 1992; 89: 8403-8407.-   14. Smith K J, Reid S W, Harlos K, McMichael A J, Stuart D I, Bell J    I, Jones E Y. Bound water structure and polymorphic amino acids act    together to allow the binding of different peptides to MHC class I    HLA-B53. Immunity 1996; 4:215-228.-   15. Levy Y, Onuchic J N. Water and proteins: a love-hate    relationship. Proc Natl Acad Sci USA 2004; 101:3325-3326.-   16. Gilis D, Rooman M. Stability changes upon mutation of    solvent-accessible residues in proteins evaluated by    database-derived potentials. J Mol Biol 1996; 257:1112-1126.-   17. Sippl M J, Ortner M, Jaritz M, Lackner P, Flockner H. Helmholtz    free energies of atom pair interactions in proteins. Fold Des 1996;    1:289-298.-   18. Jernigan R L, Bahar I. Structure-derived potentials and protein    simulations. Curr Opin Struct Biol 1996; 6:195-209.-   19. Böhm H J. The development of a simple empirical scoring function    to estimate the binding constant for a protein-ligand complex of    known three-dimensional structure. J Comput Aid Mol Des 1994;    8:243-256.-   20. Weng Z, Vajda S, DeLisi C. Prediction of complexes using    empirical free energy functions. Protein Sci 1996; 5:614-626.-   21. Vajda S, Sippl M, Novotny J. Empirical potentials and functions    for protein folding and binding. Curr Opin Struct Biol 1997;    7:222-228.-   22. Wang R, Lai L, Wang S. Further development and validation of    empirical scoring functions for structure-based binding affinity    prediction. J Comput Aid Mol Des 2002; 16:11-26.-   23. Doytchinova I A, Flower D R. Physicochemical explanation of    peptide binding to HLA-A*0201 major histocompatibility complex: a    three-dimensional quantitative structure-activity relationship    study. Proteins 2002; 48:505-518.-   24. Guerois R, Nielsen J E, Serrano L. Predicting changes in the    stability of proteins and protein complexes: a study of more than    1000 mutations. J Mol Biol 2002; 320:369-387.-   25. Froloff N, Windemuth A, Honig B. On the calculation of binding    free energies using continuum methods: application to MHC class I    protein-peptide interactions. Protein Sci. 1997; 6:1293-1301.-   26. Rognan D, Lauemøller S L, Holm A, Buus S, Tschinke V. Predicting    binding affinities of protein ligands from three-dimensional models:    application to peptide binding to class I major histocompatibility    proteins. J Med Chem 1999; 42:4650-4658.-   27. Schapira M, Totrov M, Abagyan R. Prediction of the binding    energy for small molecules, peptides and proteins. J Mol Recognit    1999; 12:177-190.-   28. Engelhard V H. Structure of peptides associated with MHC class I    molecules. Curr Opin Immunol 1994; 6:13-23.-   29. Bjorkman P J, Saper M A, Samraoui B, Bennett W S, Strominger J    L, Wiley D C. The foreign antigen binding site and T cell    recognition regions of class I histocompatibility antigens. Nature    1987; 329:512-528.-   30. Berman H M, Westbrook J, Feng Z, Gilliland G, Bhat T N, Weissig    H, Shindyalov I N, Bourne P E. The Protein Data Bank. Nucleic Acids    Res 2000; 28:235-242.-   31. Saper M A, Bjorkman P J, Wiley D C. Refined structure of the    human histocompatibility antigen HLA-A2 at 2.6 Å. J Mol Biol 1991;    219:277-319.-   32. Ruppert J, Sidney J, Celis E, Kubo R T, Grey H M, Sette A.    Prominent role of secondary anchor residues in peptide binding to    HLA-A2.1 molecules. Cell 1993; 74:929-937.-   33. Matsumura M, Fremont D H, Peterson P A, Wilson I A. Emerging    principles for the recognition of peptide antigens by MHC class I    molecules. Science 1992; 257:927-934.-   34. Ogata K, Jaramillo A, Cohen W, Briand J P, Connan F, Choppin J,    Muller S, Wodak S J. Automatic sequence design of major    histocompatibility complex class I binding peptides impairing CD8⁺ T    cell recognition. J Biol Chem 2003; 278:1281-1290.-   35. Gordon D B, Marshall S A, Mayo S L. Energy functions for protein    design. Curr Opin Struct Biol 1999; 9:509-513.-   36. Brooks B R, Bruccoleri R E, Olafson B D, States D J, Swaminathan    S, Karplus M. CHARMM: a program for macromolecular energy    minimization and dynamics calculations. J Comput Chem 1983;    4:187-217.-   37. Wolfenden R, Andersson L, Cullis P M, Southgate C C. Affinities    of amino acid side chains for solvent water. Biochemistry 1981;    20:849-855.-   38. Ooi T, Oobatake M, Nemethy G, Scheraga H A. Accessible surface    areas as a measure of the thermodynamic parameters of hydration of    peptides. Proc Natl Acad Sci USA 1987; 84:3086-3090.-   39. Wimley W C, Creamer T P, White S H. Solvation energies of amino    acid side chains and backbone in a family of host-guest    pentapeptides. Biochemistry 1996; 35:5109-5124.-   40. Desmet J, Spriet J, Lasters I. Fast and accurate side-chain    topology and energy refinement (FASTER) as a new method for protein    structure optimization. Proteins 2002; 48:31-43.-   41. Desmet J, De Maeyer M, Spriet J, Lasters I. Flexible docking of    peptide ligands to proteins. In: Webster D, ed. Methods in Molecular    Biology, vol. 143: Protein Structure Prediction: Methods and    Protocols. Totowa, N.J.: Humana Press 2000. p 359-376.-   42. Eisenberg D, McLachlan A D. Solvation energy in protein folding    and binding. Nature 1986; 319:199-203.-   43. Lazaridis T, Karplus M. Effective energy function for proteins    in solution. Proteins 1999; 35:133-152.-   44. Warshel A, Levitt M. Theoretical studies of enzymatic reactions:    dielectric, electrostatic and steric stabilization of the carbonium    ion in the reaction of lysozyme. J Mol Biol 1976; 103:227-249.-   45. Jorgensen W L, Chandrasekhar J, Madura J D, Impey R W, Klein    M L. Comparison of simple potential functions for simulating liquid    water. J Chem Phys 1983; 79:926-935.-   46. van der Burg S H, Ras E, Drijfhout J W, Benckhuijsen W E,    Bremers A J, Melief C J M, Kast W M. An HLA class I peptide-binding    assay based on competition for binding to class I molecules on    intact human B cells. Identification of conserved HIV-1 polymerase    peptides binding to HLAA*0301. Hum Immunol 1995; 44:189-98.-   47. Kessler J H, Mommaas B, Mutis T, Huijbers I, Vissers D,    Benckhuijsen W E, Schreuder G M, Offring a R, Goulmy E, Melief C J,    van der Burg S H, Drijfhout J W. Competition-based cellular peptide    binding assays for 13 prevalent HLA class I alleles using    fluorescein-labeled synthetic peptides. Hum Immunol 2003; 64:245-55.-   48. Falk K, Rötzschke O, Stevanovic S, Jung G, Rammensee H G. Allele    specific motifs revealed by sequencing of self-peptides eluted from    MHC molecules. Nature 1991; 351:290-296.-   49. Rötzschke O, Falk K, Stevanovic S, Jung G, Rammensee H G.    Peptide motifs of closely related HLA class I molecules encompass    substantial differences. Eur J Immunol 1992; 22:2453-2456.-   50. Marsh S G E, Parham P, Barber L D. The HLA FactsBook. San Diego:    Academic Press 2000.-   51. Parker K C, Bednarek M A, Coligan J E. Scheme for ranking    potential HLA-A2 binding peptides based on independent binding of    individual peptide side-chains. J Immunol 1994; 152:163-175.-   52. Parker K C, Shields M, DiBrino M, Brooks A, Coligan J E. Peptide    binding to MHC class I molecules: implications for antigenic peptide    prediction Immunol Res 1995; 14:34-57.-   53. Rudolf M P, Man S, Melief C J M, Sette A, Kast W M. Human T-cell    responses to HLA-A-restricted high binding affinity peptides of    human papillomavirus type 18 proteins E6 and E7. Clin Cancer Res    2001; 7(3Suppl):788s-795s.-   54. Rammensee H-G, Bachmann J, Emmerich N P, Bachor O A,    Stevanovic S. SYFPEITHI: database for MHC ligands and peptide motifs    Immunogenetics 1999; 50:213-219.-   55. Sidney J, Southwood S, del Guercio M F, Grey H M, Chesnut R W,    Kubo R T, Sette A. Specificity and degeneracy in peptide binding to    HLA-B7-like class I molecules. J Immunol 1996; 157:3480-3490.-   56. Sette A, Kubo R T, Sidney J, Celis E, Grey H M, Southwood S.    HLA-binding peptides and their uses. 1999; WO 99/45954.-   57. Stern H A, Kaminski G A, Banks J L, Zhou R, Berne B J, Friesner    R A. Fluctuating charge, polarizable dipole, and combined models:    parameterization from ab initio quantum chemistry. J Phys Chem B    1999; 103:4730-4737.-   58. Doytchinova I A, Blythe M J, Flower D R. Additive method for the    prediction of protein-peptide binding affinity. Application to the    MHC class I molecule HLA-A*0201. J Proteome Res 2002; 1:263-272.-   59. Peters B, Tong W, Sidney J, Sette A, Weng Z. Examining the    independent binding assumption for binding of peptide epitopes to    MHC-I molecules. Bioinformatics 2003; 19:1765-1772.

1.-14. (canceled)
 15. A method for determining an affinity score for aprotein/ligand complex useful for estimating the affinity of the ligandfor the protein, wherein said score is solely based on structural dataand a force field function, the method comprising: (a) estimatingdesolvation energy for the ligand by a method comprising in silicosimulation of immersion of the individual amino acids present in theligand as amino acid model compounds in a solvent box filled withexplicit water molecules, and applying the force field function toestimate the desolvation energy; (b) calculating ligand/protein complexinteraction energy by a method comprising modeling with a computer astructural representation of the ligand placed in a binding site of theprotein and applying the same force field function as used to estimatethe desolvation energy in order to calculate the ligand/proteininteraction energy; and (c) subtracting the desolvation energy of step(a) from the ligand/protein interaction energy of step (b) to obtainsaid affinity score.
 16. The method of claim 15, further comprisingcalculating a conformational strain energy for the protein/ligandrepresentation of step (b) by a method comprising applying the sameforce field function; and calculating said affinity score by subtractingthe desolvation energy of step (a) and the conformational strain energyfrom the ligand/protein interaction energy of step (b).
 17. The methodof claim 16 wherein the conformational strain energy of step (b) iscalculated as the difference between the sum of conformational energiesof the ligand and protein in an unbound reference state and the sum ofthe conformational energies of the ligand and the protein in theprotein/ligand representation of step (b).
 18. The method of claim 15,wherein the protein/ligand representation of step (b) is derived from anexperimentally determined structure.
 19. The method of claim 15, whereinthe modeling by the computer in step (b) comprises an energyminimization step.
 20. The method of claim 15, wherein therepresentation of the protein in step (b) is derived from anexperimentally determined structure.
 21. The method of claim 15, whereinthe protein to which the ligand binds is a receptor.
 22. The method ofclaim 15, wherein the ligand is a peptide, a small molecule or apharmacophore.
 23. The method of claim 15, wherein calculating theaffinity score further comprises combining desolvation energy andprotein-ligand complex energy, and wherein both desolvation energy andprotein-ligand complex energy are derived from the same force fieldfunction.
 24. The method of claim 15, wherein calculating the affinityscore further comprises combining desolvation energy and receptor-ligandcomplex energy, and wherein both desolvation energy and protein-ligandcomplex energy are derived from the same force field.
 25. The method ofclaim 15, wherein calculating the affinity score further comprisesdetermining the affinity score for an anchor residue in theprotein/ligand complex.
 26. The method of claim 25, wherein theprotein-ligand complex comprises a MHC receptor/ligand complex.
 27. Amethod for determining an affinity score of an anchor residues in aprotein/ligand complex, wherein said score is solely based on structuraldata and a force field, this method comprising (a) estimatingdesolvation energy for the anchor residue by a method comprising insilico simulation of individual amino acid model compounds in a solventbox filled with explicit water molecules, and applying the force fieldfunction to estimate the desolvation energy for the anchor residue; (b)calculating by a ligand-protein interaction energy at the anchor residuepositions by a method comprising modeling with a computer a structuralrepresentation of the ligand placed in the binding site of the proteinand applying the force field function to calculate the ligand-proteininteraction energy for the anchor residue; (c) calculating by modelingwith a computer a conformational strain energy at the anchor residuepositions for the protein/ligand representation of step (b) and applyingthe force field function to calculate the conformational strain energyat the anchor residue position; and (d) calculating said affinity scorefor the anchor residues by subtracting the desolvation energy of step(a) and the conformational strain energy of step (c) from theligand-protein interaction energy of step (b).
 28. The method of claim27, wherein the conformational strain energy of step (c) is calculatedas the difference between the sum of conformational energies of theligand and protein in an unbound reference state and the sum of theconformational energies of the ligand and the protein in theprotein/ligand representation of step (b).
 29. The method of claim 21,wherein the receptor is a MHC receptor.
 30. The method of claim 25,wherein the affinity score of each anchor residue is calculated bycomputer modeling that includes amino acid side-chain rotamericanalysis.
 31. The method of claim 30, wherein the computer modelingcomprises individually substituting the amino acid side-chain at eachanchor residue to form a rotameric variant and computing a total energyscore for the rotameric variant.
 32. The method of claim 31, wherein thetotal energy score is computed according to the following formula:E _(total)=min_(r) {E _(desolv)(r)+E _(inter)(r)+E _(strain)(r)};wherein r is the rotameric variant, E_(desolv) is desolvation energy,E_(inter) is side-chain/receptor non-bonded interactions, and E_(strain)is conformational strain energy.
 33. The method of claim 32, whereinE_(desolv) is −Σ_(i)% BSA(i)×GSP(i) and % BSA is percentage buriedsurface area, GSP is group solvation parameters, and i is a function ofthe substituted amino acid side-chain selected from aliphaticC_(x)H_(y), aromatic C_(x)H_(y), aromatic N_(x)H_(y), hydroxyl, sulphur,sulphydryl, charged amine, carboxyl, amide, or guanidinium.
 34. Themethod of claim 32, wherein E_(inter) isΣ{E_(vdw)(i)+E_(hbo)(i)+E_(ele)(i)} and E_(vdw) is Van der Waalsinteractions, E_(hbo) is H-bond interactions, E_(ele) is electrostaticinteractions, and i is a function of the substituted amino acidside-chain selected from aliphatic C_(x)H_(y), aromatic C_(x)H_(y),aromatic N_(x)H_(y), hydroxyl, sulphur, sulphydryl, charged amine,carboxyl, amide, or guanidinium.
 35. The method of claim 32, whereinE_(strain) is E_(rec)+E_(pep)+E_(self) and E_(rec) is strain energy inthe receptor, E_(pep) is strain of the peptide minus strain of thesubstituted amino acid side-chain, and E_(self) is self-tension of thesubstituted amino acid side-chain.
 36. The method of claim 27, whereinstep (a) further comprises calculating the % buried surface area (% BSA)for the anchor residue by a method comprising modeling with a computer astructural representation of the ligand placed in the binding site ofthe protein.